Nonparametric specification tests for conditional duration models

This paper deals with the testing of autoregressive conditional duration (ACD) models by gauging the distance between the parametric density and hazard rate functions implied by the duration process and their non-parametric estimates. We derive the asymptotic justification using the functional delta method for fixed and gamma kernels, and then investigate the finite-sample properties through Monte Carlo simulations. Although our tests display some size distortion, bootstrapping suffices to correct the size without compromising their excellent power. We show the practical usefulness of such testing procedures for the estimation of intraday volatility patterns.

Nonparametric entropy-based tests of independence between stochastic processes

This paper develops nonparametric tests of independence between two stationary stochastic processes. The testing strategy boils down to gauging the closeness between the joint and the product of the marginal stationary densities. For that purpose, I take advantage of a generalized entropic measure so as to build a class of nonparametric tests of independence. Asymptotic normality and local power are derived using the functional delta method for kernels, whereas finite sample properties are investigated through Monte Carlo simulations.

On the welfare costs of business-cycle fluctuations and economic-growth variation in the 20th century

Lucas (1987) has shown a surprising result in business-cycle research: the welfare cost of business cycles are very small. Our paper has several original contributions. First, in computing welfare costs, we propose a novel setup that separates the effects of uncertainty stemming from business-cycle fluctuations and economic-growth variation. Second, we extend the sample from which to compute the moments of consumption: the whole of the literature chose primarily to work with post-WWII data. For this period, actual consumption is already a result of counter-cyclical policies, and is potentially smoother than what it otherwise have been in their absence. So, we employ also pre-WWII data. Third, we take an econometric approach and compute explicitly the asymptotic standard deviation of welfare costs using the Delta Method. Estimates of welfare costs show major differences for the pre-WWII and the post-WWII era. They can reach up to 15 times for reasonable parameter values -β=0.985, and ∅=5. For example, in the pre-WWII period (1901-1941), welfare cost estimates are 0.31% of consumption if we consider only permanent shocks and 0.61% of consumption if we consider only transitory shocks. In comparison, the post-WWII era is much quieter: welfare costs of economic growth are 0.11% and welfare costs of business cycles are 0.037% - the latter being very close to the estimate in Lucas (0.040%). Estimates of marginal welfare costs are roughly twice the size of the total welfare costs. For the pre-WWII era, marginal welfare costs of economic-growth and business- cycle fluctuations are respectively 0.63% and 1.17% of per-capita consumption. The same figures for the post-WWII era are, respectively, 0.21% and 0.07% of per-capita consumption.

On the welfare costs of business-cycle fluctuations and economic-growth variation in the 20th century

Lucas(1987) has shown a surprising result in business-cycle research: the welfare cost of business cycles are very small. Our paper has several original contributions. First, in computing welfare costs, we propose a novel setup that separates the effects of uncertainty stemming from business-cycle uctuations and economic-growth variation. Second, we extend the sample from which to compute the moments of consumption: the whole of the literature chose primarily to work with post-WWII data. For this period, actual consumption is already a result of counter-cyclical policies, and is potentially smoother than what it otherwise have been in their absence. So, we employ also pre-WWII data. Third, we take an econometric approach and compute explicitly the asymptotic standard deviation of welfare costs using the Delta Method. Estimates of welfare costs show major diferences for the pre-WWII and the post-WWII era. They can reach up to 15 times for reasonable parameter values = 0:985, and = 5. For example, in the pre-WWII period (1901-1941), welfare cost estimates are 0.31% of consumption if we consider only permanent shocks and 0.61% of consumption if we consider only transitory shocks. In comparison, the post-WWII era is much quieter: welfare costs of economic growth are 0.11% and welfare costs of business cycles are 0.037% the latter being very close to the estimate in Lucas (0.040%). Estimates of marginal welfare costs are roughly twice the size of the total welfare costs. For the pre-WWII era, marginal welfare costs of economic-growth and business-cycle uctuations are respectively 0.63% and 1.17% of per-capita consumption. The same gures for the post-WWII era are, respectively, 0.21% and 0.07% of per-capita consumption.

Non- parametric specification tests for conditional duration models

This paper deals with the estimation and testing of conditional duration models by looking at the density and baseline hazard rate functions. More precisely, we foeus on the distance between the parametric density (or hazard rate) function implied by the duration process and its non-parametric estimate. Asymptotic justification is derived using the functional delta method for fixed and gamma kernels, whereas finite sample properties are investigated through Monte Carlo simulations. Finally, we show the practical usefulness of such testing procedures by carrying out an empirical assessment of whether autoregressive conditional duration models are appropriate to oIs for modelling price durations of stocks traded at the New York Stock Exchange.